{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# `InferenceModel` API: Quick start guide\n",
        "\n",
        "> **tldr:** This notebook introduces the InferenceModel API, a purely functional and immutable interface designed for running forecasts. It wraps a standard Model but provides methods (assimilate, advance, observe) that explicitly accept and return an immutable SimulationState object, making it fully compatible with JAX transformations. You will learn how to use this functional API, apply helper functions like `api.unroll_from_advance` to generate rollouts, and leverage the `InferenceRunner` to perform scalable, embarrassingly parallel inference tasks with Apache Beam.\n",
        "\n",
        "The `InferenceModel` API is designed for users that are primarily interested in making forecasts and examining model outputs. It prioritizes \"no surprise\" behavior over flexibility.\n",
        "\n",
        "Under the hood `InferenceModel` wraps a functional representation of a `Model` instance and provides purely functional interface to methods of said model. `InferenceModel` is immutable, and is registered as a jax pytree, making it compatible with JAX transformations.\n",
        "\n",
        "Outline:\n",
        "1. `InferenceModel` and `SimulationState`\n",
        "2. Inference helper functions\n",
        "3. Scalable inference using `apache_beam`\n",
        "\n"
      ],
      "metadata": {
        "id": "tW_MyBy5E2Cs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "import dataclasses\n",
        "from typing import Callable\n",
        "\n",
        "import coordax as cx\n",
        "from flax import nnx\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "import jax_datetime as jdt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import xarray\n",
        "\n",
        "\n",
        "from neuralgcm.experimental.core import api\n",
        "from neuralgcm.experimental.core import coordinates\n",
        "from neuralgcm.experimental.core import observation_operators\n",
        "from neuralgcm.experimental.core import nnx_compat\n",
        "from neuralgcm.experimental.core import time_integrators\n",
        "from neuralgcm.experimental.core import typing\n",
        "from neuralgcm.experimental.core import module_utils\n",
        "from neuralgcm.experimental.core import xarray_utils\n",
        "from neuralgcm.experimental.inference import runner as inference_runner\n",
        "from neuralgcm.experimental.inference import dynamic_inputs as inference_dynamic_inputs\n",
        "from neuralgcm.experimental.toy_model_examples import lorenz96\n",
        "\n",
        "import logging\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "logging.getLogger('jax._src.lib.xla_bridge').addFilter(lambda _: False)"
      ],
      "metadata": {
        "id": "PNh6XimIK8gw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## `InferenceModel` and `SimulationState`\n",
        "\n",
        "**Key concepts:**\n",
        "* `InferenceModel` is an immutable object providing functional interface to the underlying model methods\n",
        "* `SimulationState` object holds all simulation variables and is passed as an explicit argument to `InferenceModel` methods\n",
        "* If some simulation variables are missing, `InferenceModel` attempts to raise an informative error"
      ],
      "metadata": {
        "id": "CmDTsAaoK_Pl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### InferenceModel demo using Lorenz-96 system\n",
        "\n",
        "As in the `Model` API notebook, we will use the Lorenz-96 dynamical system, this time loaded from `toy_model_examples` module."
      ],
      "metadata": {
        "id": "QVmy-v_ngqbV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's instantiate an inference model. We do so via `from_model_api` classmethod."
      ],
      "metadata": {
        "id": "VBqABVcKnEoR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k = cx.LabeledAxis('k', np.arange(36))\n",
        "j = cx.LabeledAxis('j', np.arange(8))\n",
        "\n",
        "l96_model = lorenz96.Lorenz96WithTwoScales(k, j, forcing=nnx.Param(10.0))\n",
        "l96_inference_model = api.InferenceModel.from_model_api(l96_model)"
      ],
      "metadata": {
        "id": "saThMuTcmGAQ",
        "outputId": "cd9bbd45-8695-4688-977e-09e5e0dd6623"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Under the hood, inference model contains:\n",
        "* Model graph definition\n",
        "* Model state (trainable parameters, constant arrays etc.)\n",
        "* Dummy structure representing the expected simulation state structure\n",
        "* Optional fiddle configuration required for serialization"
      ],
      "metadata": {
        "id": "C82r7R_-nBnx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(l96_inference_model.__dict__.keys())"
      ],
      "metadata": {
        "id": "OXKLTlS2m3WZ",
        "outputId": "01f11d43-3aa2-47d9-e241-7cc689404f61"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['model_graph_def', 'model_state', 'dummy_simulation_state', 'fiddle_config'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l96_inference_model.model_state  # contains single trainable parameter `forcing`"
      ],
      "metadata": {
        "id": "T-Vdh-FRnnbU",
        "outputId": "891d6bda-cd8c-4f0b-c1d2-105eff8707f6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State({\n",
              "  'forcing': Param(\n",
              "    value=10.0\n",
              "  )\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`InferenceModel` provides core methods similar to Model, but its methods are purely functional: they explicitly accept and return the required simulation state. For this reason initialization of random processes (via `rng` argument) and dynamic inputs a.k.a. forcings (via 'dynamic_inputs` argument) is done as a part of core methods.\n",
        "*   **`assimilate`**: Takes `inputs`, `dynamic_inputs`, `rng`. Returns a `SimulationState`.\n",
        "*   **`advance`**: Takes `simulation_state` and `dynamic_inputs`. Returns a `SimulationState`.\n",
        "*   **`observe`**: Takes `simulation_state`, `query`, and `dynamic_inputs`. Returns a dictionary of dictionaries containing `cx.Field` objects.\n",
        "\n",
        "Some of these methods have `None` defaults to simplify calls for models that do not require features like `dynamic_inputs` or `rng`.\n",
        "\n",
        "`SimulationState` is a container that holds core simulation variables:\n",
        "\n",
        "```python\n",
        "@dataclasses.dataclass(frozen=True)\n",
        "class SimulationState(Generic[State]):\n",
        "  \"\"\"Simulation state decomposed into prognostic, diagnostic and randomness.\n",
        "\n",
        "  Attributes:\n",
        "    prognostics: Prognostic state of the simulation.\n",
        "    diagnostics: Optional diagnostic state of the simulation.\n",
        "    randomness: Optional state of random processes in the simulation.\n",
        "    extras: Optional additional simulation state variables.\n",
        "  \"\"\"\n",
        "\n",
        "  prognostics: State\n",
        "  diagnostics: State\n",
        "  randomness: State\n",
        "  extras: dict[str, State] = dataclasses.field(default_factory=dict)\n",
        "```\n",
        "\n",
        "Model developers can add additional types to be separated out of the model by updating `simulation_state_filters` property on their `Model` implementation."
      ],
      "metadata": {
        "id": "tUNOJsVhn1Vw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparing inputs for model initialization\n",
        "kj = cx.compose_coordinates(k, j)\n",
        "rng = np.random.RandomState(0)\n",
        "x_init = abs(10 * np.sin(np.linspace(0, 13 * 2*np.pi, k.sizes['k'])))\n",
        "x_init[0] += 0.01\n",
        "t0 = jdt.Datetime.from_isoformat('2000-01-01')\n",
        "inputs = {\n",
        "    'slow': {'x': cx.wrap(x_init, k), 'time': cx.wrap(t0)},\n",
        "    'fast': {'y': cx.wrap(rng.uniform(size=kj.shape, low=-0.5, high=0.5), kj)},\n",
        "}\n",
        "# assimilate expects at least a dummy timedelta dimension, we add it here.\n",
        "t_del = coordinates.TimeDelta(np.zeros(1) * np.timedelta64(1, 'h'))\n",
        "add_td = lambda f: f.broadcast_like(cx.compose_coordinates(t_del, f.coordinate))\n",
        "inputs = jax.tree.map(add_td, inputs, is_leaf=cx.is_field)\n",
        "\n",
        "# Assimilating inputs returns current simulation state.\n",
        "sim_state = l96_inference_model.assimilate(inputs)"
      ],
      "metadata": {
        "id": "L3HORIlUnxLv"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sim_state"
      ],
      "metadata": {
        "id": "BAS1EFFNpg1t",
        "outputId": "c6680039-2a30-4ec8-fc17-cac8c5e9767b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SimulationState(prognostics=State({\n",
              "  'time': Prognostic( # 2 (8 B)\n",
              "    value=<Field dims=() shape=() axes={} >\n",
              "  ),\n",
              "  'x': Prognostic( # 36 (144 B)\n",
              "    value=<Field dims=('k',) shape=(36,) axes={'k': LabeledAxis} >\n",
              "  ),\n",
              "  'y': Prognostic( # 288 (1.2 KB)\n",
              "    value=<Field dims=('k', 'j') shape=(36, 8) axes={'j': LabeledAxis, 'k': LabeledAxis} >\n",
              "  )\n",
              "}), diagnostics=State({}), randomness=State({}), extras={'coupling': State({})})"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`advance` computes a new simulation state given the previous without any side effects"
      ],
      "metadata": {
        "id": "SKbi49tGqtcO"
      }
    },
    {
      "metadata": {
        "id": "HPgCXuWM-YCs"
      },
      "cell_type": "code",
      "source": [
        "sim_state.prognostics['time'].data"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "jax_datetime.Datetime(delta=jax_datetime.Timedelta(days=10957, seconds=0))"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "execution_count": 69
    },
    {
      "cell_type": "code",
      "source": [
        "# Running advance on `InferenceModel`.\n",
        "next_sim_state = sim_state\n",
        "for _ in range(1300):  # burn in to get more interesting states.\n",
        "  next_sim_state = l96_inference_model.advance(next_sim_state)"
      ],
      "metadata": {
        "id": "QOtO5L3oryu9"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`observe` computes observation for a given `query`"
      ],
      "metadata": {
        "id": "ktbchqMgq98v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "observation = l96_inference_model.observe(next_sim_state, {'slow': {'x': k}})\n",
        "observation['slow']['x'].to_xarray().plot(x='k')"
      ],
      "metadata": {
        "id": "aXYb4PJfqmZ8",
        "colab": {
          "height": 295
        },
        "outputId": "ca28729a-5dc7-41d3-8cbe-988f1b758d91"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x3338a40c3950>]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 263,
              "width": 370
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference helper functions\n",
        "\n",
        "**Key concepts:**\n",
        "* `api.unroll_from_advance` and `api.forecast_steps` are helper functions that make fixed length rollouts\n",
        "* `InferenceModel` is safer to use with forecast helpers as it does not result in any side effects"
      ],
      "metadata": {
        "id": "tPgOIABMo4xt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us make a prediction from the model using the state we obtained above using `api.unroll_from_advance`:"
      ],
      "metadata": {
        "id": "FmUbwhfPuOMA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "timedelta_between_saves = l96_inference_model.timestep * 5\n",
        "final_state, trajectory = api.unroll_from_advance(\n",
        "    l96_inference_model,\n",
        "    initial_state=next_sim_state,\n",
        "    timedelta=timedelta_between_saves,\n",
        "    steps=100,\n",
        "    query={'slow': {'x': k}}\n",
        ")"
      ],
      "metadata": {
        "id": "J0iu4JYnrtse"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = xarray_utils.nested_fields_to_xarray(trajectory)\n",
        "# imshow isn't happy with timedelta64 coords, so we convert it to seconds here.\n",
        "datasets['slow'].coords['timedelta'] = datasets['slow'].timedelta / np.timedelta64(1, 's')\n",
        "datasets['slow'].x.plot.imshow(x='k', y='timedelta')\n",
        "# here we have timedelta set in seconds (with single timestep being 864)."
      ],
      "metadata": {
        "id": "6dOaqyI_svjr",
        "colab": {
          "height": 299
        },
        "outputId": "6220b7ff-491a-4315-ea4d-92d3ef99c46c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x3338a40c3a10>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 410
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another helper `api.forecast_steps` combines the initial assimilation step with the rollout:"
      ],
      "metadata": {
        "id": "LRn4-boCuMg4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "another_final_state, another_trajectory = api.forecast_steps(\n",
        "    l96_inference_model,\n",
        "    inputs=inputs,\n",
        "    timedelta=timedelta_between_saves,\n",
        "    steps=100,\n",
        "    query={'slow': {'x': k}}\n",
        ")\n",
        "another_datasets = xarray_utils.nested_fields_to_xarray(another_trajectory)\n",
        "another_datasets['slow'].coords['timedelta'] = another_datasets['slow'].timedelta / np.timedelta64(1, 's')\n",
        "another_datasets['slow'].x.plot.imshow(x='k', y='timedelta')"
      ],
      "metadata": {
        "id": "L51WLejruMNL",
        "colab": {
          "height": 299
        },
        "outputId": "12298b11-6a8c-4919-a282-caacab7c9d12"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x3338a1f4dc40>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 410
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could repeat this simulation or any previous with the same `l96_inference_model` and get exactly same result."
      ],
      "metadata": {
        "id": "T-TrGI0ktelp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newly_assimilated = l96_inference_model.assimilate(inputs)\n",
        "\n",
        "np.testing.assert_allclose(\n",
        "    newly_assimilated.prognostics['x'].data,\n",
        "    sim_state.prognostics['x'].data,\n",
        ")"
      ],
      "metadata": {
        "id": "OIwJa7NyteLK"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scalable inference using InferenceRunner\n",
        "\n",
        "**Key conepts:**\n",
        "* `InferenceModel` can be used with `InferenceRunner` to run embarrassingly parallel inference tasks using Apache Beam."
      ],
      "metadata": {
        "id": "rbkIobspFQrK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's generate a \"ground truth\" reference dataset. We will use this dataset as the source for our initial conditions."
      ],
      "metadata": {
        "id": "D9puP1JPZAvW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_, trajectory = api.unroll_from_advance(\n",
        "    l96_inference_model,\n",
        "    initial_state=next_sim_state,\n",
        "    timedelta=(l96_inference_model.timestep * 25),\n",
        "    steps=4*100,  # 100 days.\n",
        "    query={'slow': {'x': k, 'time': cx.Scalar()}, 'fast': {'y': kj, 'time': cx.Scalar()}},\n",
        ")\n",
        "ground_truth_data = xarray_utils.nested_fields_to_xarray(trajectory)\n",
        "# Swap the 'timedelta' dimension with the 'time' coordinate and set 'time' as the index\n",
        "ground_truth_data['slow'] = ground_truth_data['slow'].swap_dims({'timedelta': 'time'}).set_coords('time')\n",
        "ground_truth_data['fast'] = ground_truth_data['fast'].swap_dims({'timedelta': 'time'}).set_coords('time')"
      ],
      "metadata": {
        "id": "PMcxTLloYkSa"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us setup inference parameters for `runner.InferenceRunner` and show how to run it directly from a python interpreter"
      ],
      "metadata": {
        "id": "gAGce5kcacDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output_path = './reforecasts.zarr'\n",
        "output_query = {'slow': {'x': k}}\n",
        "init_times = pd.date_range(\n",
        "    pd.Timestamp('2000-01-14'),\n",
        "    pd.Timestamp('2000-04-14'),\n",
        "    freq=pd.Timedelta('1 day'),\n",
        "    inclusive='left',\n",
        ").values\n",
        "\n",
        "zarr_chunks = {'lead_time': 1, 'init_time': 1}\n",
        "runner = inference_runner.InferenceRunner(\n",
        "    model=l96_inference_model,\n",
        "    inputs=ground_truth_data,\n",
        "    dynamic_inputs=inference_dynamic_inputs.EmptyDynamicInputs(),\n",
        "    init_times=init_times,\n",
        "    ensemble_size=1,\n",
        "    zarr_chunks=zarr_chunks,\n",
        "    output_path=output_path,\n",
        "    output_query=output_query,\n",
        "    output_freq=np.timedelta64(6, 'h'),\n",
        "    output_duration=np.timedelta64(48, 'h'),\n",
        "    write_duration=np.timedelta64(24, 'h'),\n",
        "    unroll_duration=np.timedelta64(24, 'h'),\n",
        "    checkpoint_duration=np.timedelta64(24, 'h'),\n",
        ")\n",
        "runner.setup()\n",
        "for task_id in range(runner.task_count):\n",
        "  runner.run(task_id)"
      ],
      "metadata": {
        "id": "04WhgoKyafUd"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "metadata": {
        "id": "38i0fuusM-gl"
      },
      "cell_type": "markdown",
      "source": [
        "For the inference tasks to be well defined `output_freq`, `output_duration` and\n",
        "other parameters must be congruent. See the InferenceRunner docstring for details."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_tree = xarray.open_datatree(output_path)"
      ],
      "metadata": {
        "id": "1QPRBkUenvmb"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_tree.coords['lead_time'] = data_tree.lead_time / np.timedelta64(1, 's')\n",
        "data_tree['slow'].isel(init_time=[0, 10, 20, 30, -10], realization=0).x.plot.imshow(x='k', y='lead_time', col='init_time')"
      ],
      "metadata": {
        "colab": {
          "height": 240
        },
        "id": "H25YeCXooSpe",
        "outputId": "92ceb514-3695-4a52-abd4-4342443f7926"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x3338a24759d0>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x216 with 6 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 207,
              "width": 1032
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we repeat the process, but run it as a beam pipeline. Here it won't be faster since we are using `DirectRunner`, but this can scale to as many processes as the number of independent rollouts."
      ],
      "metadata": {
        "id": "OGKwnKLJKj3L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output_path = './reforecasts_beam.zarr'\n",
        "output_query = {'slow': {'x': k}}\n",
        "init_times = pd.date_range(\n",
        "    pd.Timestamp('2000-01-14'),\n",
        "    pd.Timestamp('2000-04-14'),\n",
        "    freq=pd.Timedelta('1 day'),\n",
        "    inclusive='left',\n",
        ").values\n",
        "\n",
        "zarr_chunks = {'lead_time': 1, 'init_time': 1}\n",
        "runner = inference_runner.InferenceRunner(\n",
        "    model=l96_inference_model,\n",
        "    inputs=ground_truth_data,\n",
        "    dynamic_inputs=inference_dynamic_inputs.EmptyDynamicInputs(),\n",
        "    init_times=init_times,\n",
        "    ensemble_size=1,\n",
        "    zarr_chunks=zarr_chunks,\n",
        "    output_path=output_path,\n",
        "    output_query=output_query,\n",
        "    output_freq=np.timedelta64(6, 'h'),\n",
        "    output_duration=np.timedelta64(48, 'h'),\n",
        "    write_duration=np.timedelta64(24, 'h'),\n",
        "    unroll_duration=np.timedelta64(24, 'h'),\n",
        "    checkpoint_duration=np.timedelta64(24, 'h'),\n",
        ")\n",
        "runner.setup()"
      ],
      "metadata": {
        "id": "uxShA5zNoy2X"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XqC_9GLlVSyt"
      },
      "cell_type": "code",
      "source": [
        "! pip install apache_beam"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "rd-t04nAVRMv"
      },
      "cell_type": "code",
      "source": [
        "import apache_beam as beam\n",
        "from apache_beam.options.pipeline_options import PipelineOptions"
      ],
      "outputs": [],
      "execution_count": 89
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "height": 17
        },
        "id": "9f06ebce",
        "outputId": "28bdd8ba-6232-46a1-f59d-d17cd099752d"
      },
      "source": [
        "with beam.Pipeline(options=PipelineOptions(runner='DirectRunner')) as pipeline:\n",
        "  _ = (\n",
        "      pipeline\n",
        "      | 'Create Tasks' >> beam.Create(range(runner.task_count))\n",
        "      | 'Run Tasks' >> beam.Map(runner.run)\n",
        "  )"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_tree = xarray.open_datatree(output_path)\n",
        "data_tree.coords['lead_time'] = data_tree.lead_time / np.timedelta64(1, 's')\n",
        "data_tree['slow'].isel(init_time=[0, 10, 20, 30, -10], realization=0).x.plot.imshow(\n",
        "    x='k', y='lead_time', col='init_time'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "height": 240
        },
        "id": "tmrxXiG7pGB8",
        "outputId": "1bdcbdf6-6b01-4595-d3a1-e09b80ce4ce1"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.plot.facetgrid.FacetGrid at 0x3338f2e24ec0>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x216 with 6 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 207,
              "width": 1032
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GP8Ej-9iH6zv"
      },
      "cell_type": "markdown",
      "source": [
        "Similar approach can be used with Array jobs on traditional HPC setup.\n",
        "\n",
        "In setup.py\n",
        "```python\n",
        "runner = InferenceRunner(...)\n",
        "runner.setup()\n",
        "```\n",
        "In task.py\n",
        "```python\n",
        "runner = InferenceRunner(...)\n",
        "runner.run(int(os.environ['SLURM_ARRAY_TASK_ID']))\n",
        "```"
      ]
    },
    {
      "metadata": {
        "id": "Q9FVpik_IVn7"
      },
      "cell_type": "markdown",
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
